Reliability Evaluation of Phasor Measurement Unit Considering Failure of Hardware and Software Using Fuzzy Approach

The wide-area measurement system (WAMS) consists of the future power system, increasing geographical sprawl which is linked by the Phasor measurement unit(PMU). Thus, the failure of PMU will cause severe results, such as a blackout of the power system. In this paper, the reliability model of PMU is considered both hardware and software, where it gives a characteristic of correlated failure of hardware and software. Markov process is applied to model PMU, and reliability parameters are given by using symmetrical triangular membership for Type-1 fuzzy reliability analysis. The paper gives insightful results revealing the effective approach for analyzing the reliability of PMU, under a circumstance which lack of sufficient field data

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

OUT-OF-STEP DETECTION BASED ON ZUBOV’S APPROXIMATION BOUNDARY METHOD

Disturbances in power systems may lead to electromagnetic transient oscillations due to mismatch of mechanical input power and electrical output power. Out-of-step conditions in power system are common after the disturbances where the continuous oscillations do not damp out and the system becomes unstable. Existing out-of-step detection methods are system specific as extensive off-line studies are required for setting of relays. Most of the existing algorithms also require network reduction techniques to apply in multi-machine power systems. To overcome these issues, this research applies Phasor Measurement Unit (PMU) data and Zubov’s approximation stability boundary method, which is a modification of Lyapunov’s direct method, to develop a novel out-of-step detection algorithm. The proposed out-of-step detection algorithm is tested in a Single Machine Infinite Bus system, IEEE 3-machine 9-bus, and IEEE 10-machine 39-bus systems. Simulation results show that the proposed algorithm is capable of detecting out-of-step conditions in multi-machine power systems without using network reduction techniques and a comparative study with an existing blinder method demonstrate that the decision times are faster. The simulation case studies also demonstrate that the proposed algorithm does not depend on power system parameters, hence it avoids the need of extensive off-line system studies as needed in other algorithms

Model Parameter Calibration in Power Systems

In power systems, accurate device modeling is crucial for grid reliability, availability, and resiliency. Many critical tasks such as planning or even realtime operation decisions rely on accurate modeling. This research presents an approach for model parameter calibration in power system models using deep learning. Existing calibration methods are based on mathematical approaches that suffer from being ill-posed and thus may have multiple solutions. We are trying to solve this problem by applying a deep learning architecture that is trained to estimate model parameters from simulated Phasor Measurement Unit (PMU) data. The data recorded after system disturbances proved to have valuable information to verify power system devices. A quantitative evaluation of the system results is provided. Results showed high accuracy in estimating model parameters of 0.017 MSE on the testing dataset. We also provide that the proposed system has scalability under the same topology. We consider these promising results to be the basis for further exploration and development of additional tools for parameter calibration

Doubly-fed induction generator used in wind energy

Wound-rotor induction generator has numerous advantages in wind power generation over other generators. One scheme for wound-rotor induction generator is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this work, a novel induction machine model is developed. This model includes the saturation in the main and leakage flux paths. It shows that the model which considers the saturation effects gives more realistic results. A new technique, which was developed for synchronous machines, was applied to experimentally measure the stator and rotor leakage inductance saturation characteristics on the induction machine. A vector control scheme is developed to control the rotor side voltage-source converter. Vector control allows decoupled or independent control of both active and reactive power of DFIG. These techniques are based on the theory of controlling the B- and q- axes components of voltage or current in different reference frames. In this work, the stator flux oriented rotor current control, with decoupled control of active and reactive power, is adopted. This scheme allows the independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. Conventionally, the controller type used in vector controllers is of the PI type with a fixed proportional and integral gain. In this work, different intelligent schemes by which the controller can change its behavior are proposed. The first scheme is an adaptive gain scheduler which utilizes different characteristics to generate the variation in the proportional and the integral gains. The second scheme is a fuzzy logic gain scheduler and the third is a neuro-fuzzy controller. The transient responses using the above mentioned schemes are compared analytically and experimentally. It has been found that although the fuzzy logic and neuro-fuzzy schemes are more complicated and have many parameters; this complication provides a higher degree of freedom in tuning the controller which is evident in giving much better system performance. Finally, the simulation results were experimentally verified by building the experimental setup and implementing the developed control schemes

A PROPOSED CONTROL SOLUTION FOR THE CAL POLY WIND ENERGY CAPTURE SYSTEM

The focus of this thesis is to research, analyze, and design a reliable and economical control system for the Cal Poly Wind Energy Capture System (WECS). A dynamic permanent magnet generator model is adopted from [1] and [2] and combined with an existing wind turbine model to create a non-linear time varying model in MATLAB. The model is then used to analyze potentially harmful electrical disturbances, and to define safe operating limits for the WECS. An optimal operating point controller utilizing a PID speed loop is designed with combined optimization criteria and the final controller design is justified by comparing performance measures of energy efficiency and mitigation of mechanical loads. The report also discusses implications for a WECS when blade characteristics are mismatched with the generator. Finally, possible ways to improve the performance of the Cal Poly WECS are addressed

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

In recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN are tested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed the Layer Recurrent Neural Network (LRNN) architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network

電力系統の静的および動的セキュリティ評価増強のための同期位相計測装置の最適配置

九州工業大学博士学位論文 学位記番号：工博甲第490号 学位授与年月日：令和2年3月25日1 INTRODUCTION|2 PMU-BASED POWER SYSTEM MONITORING AND CONTROL|3 OPTIMAL PMU PLACEMENT PROBLEM AND STATE ESTIMATION|4 MULTI OBJECTIVE PMU PLACEMENT WITH CURRENT CHANNEL SELECTION|5 INFLUENCE OF MEASUREMENT UNCERTAINTY PROPAGATION IN PMU PSEUDO MEASUREMENT|6 PHASOR-ASSISTED VOLTAGE STABILITY ASSESSMENT BASED ON OPTIMALLY PLACED PMUS|7 PMU PLACEMENT FOR DYNAMIC VULNERABILITY ASSESSMENT|8 CONCLUSIONS九州工業大学令和元年

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

In recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN are tested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed the Layer Recurrent Neural Network (LRNN) architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network